Collision avoidance of a mobile unit

ABSTRACT

A collision avoidance system for a mobile unit includes an image capturing unit for capturing an image of an environment surrounding a mobile unit. Motion vector is calculated based on the captured image. Collision probability is calculated based on the motion vectors of the image. The system includes a plurality of receptive field units that are modeled on the optic lobe cells of the flies. Each of the receptive field units includes a filter producing an excitatory response to the motion vector diverging from the center of the receptive field in the central area of the receptive field and producing an inhibitory response to the motion vector converging toward the center of the receptive field in the areas around the central area. The outputs of the receptive field units are compared to determine a direction in which the obstacle approaches the mobile unit. The mobile unit moves in a direction to avoid collision.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a collision avoidance system, program,and method of a mobile unit for avoiding collision with an obstacle, andmore particularly, to a collision avoidance system, program, and methodof a mobile unit which estimates a collision probability and anapproaching direction of an obstacle using a plurality of motiondetectors which are modeled on the optic lobe neurons of flies.

2. Description of the Related Art

Collision avoidance against an obstacle is a function necessary for amobile unit to arrive at a destination safely. Many techniques foravoiding collision with an obstacle have been proposed, which are basedon optical flows extracted from visual information such as camera image.

For example, Japanese Patent Application Publication No. 11-134504discloses a device which detects collision with an obstacle based on avalue obtained by subtracting a vector sum of optical flows in adirection converging to a given point from a vector sum of optical flowsin a direction diverging from the same point. A neural network is usedfor calculating the difference between the vector sum in divergingdirection and the vector sum in converging direction.

In addition, Japanese Patent Application Publication No. 2003-51016discloses a system in which vector sums of optical flows in twodifferent areas in an image are calculated respectively using spatialfilters such as Gaussian filters, and an approaching obstacle isdetected based on the difference between these vector sums.

With respect to avoidance behavior of a living body responsive to visualinformation, a living body performs wide variety of rapid and properavoidance behaviors responsive to complex external environments in thereal world. Also in a collision avoidance technique of a mobile unit, itis desirable that optimal behavior is selected according to thedirection in which an obstacle is approaching the mobile unit or thelike. However, a technique for detecting a direction in which anobstacle is approaching has not been presented by conventionaltechniques such as in the above documents.

The present invention provides a collision avoidance technique for amobile unit which allows avoidance of collision with an obstacle bydetecting a direction in which the obstacle approaches the mobile unitbased on visual information. A behavior is selected according to theapproaching direction of the obstacle.

SUMMARY OF THE INVENTION

The present invention provides a collision avoidance system for a mobileunit for avoiding collision with an obstacle that approaches the mobileunit. This system includes image capturing means for capturing an imageof an environment surrounding the mobile unit, means for calculatingmotion vectors of the image based on the image captured by the imagecapturing means, means for calculating collision probabilities of theobstacle based on the motion vectors on a plurality of pixels in theimage, and means for comparing the collision probabilities on theplurality of pixels to determine a direction in which the obstacleapproaches the mobile unit. The mobile unit is moved in a directiondifferent from the determined approaching direction.

According to this invention, a direction in which an obstacle approachesa mobile unit can be determined based on a collision probabilitycalculated on a plurality of pixels in an image, so that an optimalbehavior can be selected to avoid collision with the obstacle.

According to one embodiment of the present invention, the motion vectorcalculating means calculates a temporal correlation relative to lightand dark of two different pixels on the image. The correlation value istreated as a motion vector.

In one embodiment of the present invention, the obstacle collisionprobability calculating means uses a filter which produces an excitatoryresponse in the central area responsive to the motion vector divergingfrom the center and which produces an inhibitory response in the areaaround the central area responsive to the motion vector convergingtoward the center. The calculating means calculates the collisionprobabilities by adding a value reflecting the magnitude of the motionvector.

In one embodiment of the present invention, when one of the collisionprobabilities exceeds a predetermined threshold value, the statusdetermination means determines a direction in which the obstacleapproaches the mobile unit based on magnitudes of the collisionprobabilities in a plurality of pixels, and moves the mobile unit in adirection which is selected responsive to the determined direction.

In addition, the present invention provides a computer program foravoiding collision with an obstacle that approaches a mobile unit. Thisprogram causes a computer to perform capturing an image of anenvironment surrounding the mobile unit, calculating motion vectors onthe image based on the image captured by the image capturing function, afunction of calculating collision probabilities of the obstacle based onthe motion vectors in a plurality of pixels in the image, and comparingthe collision probabilities in the plurality of pixels to determine adirection in which the obstacle approaches the mobile unit. The mobileunit is moved in a direction different from the determined direction.

Further, the present invention provides a method for avoiding collisionwith an obstacle that approaches a mobile unit. This method includes thesteps of capturing an image of an environment surrounding the mobileunit, calculating motion vectors on the image based on the imagecaptured by the image capturing step, calculating collisionprobabilities of the obstacle based on the motion vectors in a pluralityof pixels in the image, and comparing the collision probabilities on theplurality of pixels to determine a direction in which the obstacleapproaches the mobile unit. The mobile unit is moved in a directiondifferent from the determined direction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram which shows a collision avoidance systemof a mobile unit according to one embodiment of the present invention;

FIG. 2 is a functional block diagram of the collision avoidance systemof the mobile unit according to the embodiment;

FIG. 3 is a flowchart which shows collision avoidance processing of themobile unit;

FIG. 4 is a conceptual diagram of image information obtained from a CCDcamera;

FIG. 5 is a block diagram of EMD which is applied in a motion vectorcalculation section;

FIG. 6 is a diagram which shows characteristics of a receptive fieldunit constituting a collision avoidance model applied in a collisionprobability calculation section;

FIG. 7 is a schematic diagram of the collision avoidance model appliedin the collision probability calculation section; and

FIG. 8 is a graph which shows progressions of the outputs 01(t), 02(t),03(t) in the time of the receptive field units as the obstacleapproaches the mobile unit from the front (θ=0).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Embodiments of the present invention will be described with reference tothe drawings. FIG. 1 is a schematic diagram which illustrates acollision avoidance system of a mobile unit 10 according to oneembodiment of the present invention.

In the present embodiment, the mobile unit 10 is a small autonomousmobile robot with two wheels, for example, Khepera Robot™ which ishighly versatile and widely used as a small mobile robot for experiment.The mobile unit 10 is provided with image capturing means such as a CCDcamera 12 on the main body, and recognizes an obstacle 16 around themobile unit 10 based on an image captured by the CCD camera 12. Theimage captured by the CCD camera 12 is transmitted to a collisionavoidance device 14 connected via a wired or wireless connection to themobile unit 10.

The collision avoidance device 14 analyzes the image received from themobile unit 10 to determine a direction in which the obstacle 16 isapproaching the mobile unit 10 and to determine a collision probability.When the collision avoidance device 14 determines that the obstacle 16is highly likely to collide with the mobile unit 10, the collisionavoidance device 14 provides the mobile unit 10 with a move instructionto avoid collision with the obstacle 16 according to the approachingdirection of the obstacle 16. For example, as illustrated in FIG. 1, ifthe obstacle 16 is approaching the mobile unit 10 from front of the unit(θ=0), the move instruction is selected to move the mobile unit 10 tothe forward right diagonally as indicated by the dotted arrow. Thecollision avoidance device 14 may be provided inside the mobile unit 10.

FIG. 2 is a functional block diagram of the collision avoidance systemof the mobile unit 10 according to one embodiment. The collisionavoidance system includes the CCD camera 12 and a mobile unit controller24 which are provided in the mobile unit 10, and the collision avoidancedevice 14. The collision avoidance device 14 includes a motion vectorcalculation section 18, a collision probability calculation section 20,and a status determination section 22.

Basic operation of the collision avoidance system of the mobile unit 10will be described below.

The CCD camera 12 mounted on the mobile unit 10 captures an image of anenvironment surrounding the mobile unit 10. The motion vectorcalculation section 18 analyzes the image captured by the CCD camera 12to calculate a “motion vector” which represents motion direction of eachpixel in the image. The collision avoidance calculation section 20calculates “collision probabilities” on a plurality of pixels in theimage using a collision avoidance model created based on optic lobeneurons of flies. The “collision probability” is an indicator ofprobabilities that the obstacle 16 collides with the mobile unit 10. Thestatus determination section 22 determines whether or not the obstacle16 collides with the mobile unit 10 based on the calculated collisionprobabilities, and compares the collision probabilities in the pluralityof pixels to determine a direction θ to which the mobile unit 10 shouldmove. Then, the mobile unit controller 24 moves the mobile unit 10 tothe direction θ to avoid collision with the obstacle 16.

Collision avoidance processing of the mobile unit 10 according to thepresent embodiment will next be described with reference to FIG. 3. FIG.3 is a flowchart which shows the collision avoidance processing of themobile unit 10.

In step S101, the motion vector calculation section 18 obtains an imagefrom the CCD camera 12. In the present embodiment, the image is sampledat 10 Hz by the CCD camera 12, and transmitted to the motion vectorcalculation section 18. An image captured by the CCD camera 12 is a640×480 pixel image, and each pixel in the image has a grayscale valuefrom 0 to 255. A two-dimensional coordinate system in which thehorizontal axis is x-axis and the vertical axis is y-axis in the imageis set as shown in FIG. 4, and a grayscale value of a pixel atcoordinates (x, y) at time t is expressed as i(x, y, t).

In step S103, the motion vector calculation section 18 preprocesses agrayscale value i(x, y, t) of each pixel in the image. In the presentembodiment, a grayscale value i(x, y, t) of each pixel is smoothed witha Gaussian filter. When a grayscale value of a pixel at any coordinates(x_(k), y_(l)) on the image at time t is expressed as i(x_(k), y_(l),t), a Gaussian filter output I(x, y, t) at coordinates (x, y) is givenby the following equation:

$\begin{matrix}{{I\left( {x,y,t} \right)} = {\sum\limits_{k}{\sum\limits_{l}{{i\left( {x_{k},y_{l},t} \right)}{\mathbb{e}}^{\sqrt{\frac{{({x_{k} - x})}^{2} + {({{y\;}_{l} - y})}^{2}}{2\;\sigma^{2}}}}}}}} & (1)\end{matrix}$where σ is a constant which defines a spatial expanse of the filter.

In step S105, the motion vector calculation section 18 calculates amotion vector of each pixel of the image. In the present embodiment, EMD(Elementary Movement Detector) which has been proposed as a model of anoptical motion detector of flies is applied as a technique forcalculating a motion vector. The EMD is configured as shown in FIG. 5,and detects a motion by calculating a temporal correlation betweenreceptors.

In the present embodiment, Gaussian filter's output values I(x, y, t)and I(x+1, y, t) on pixels adjacent to each other in the x-axisdirection are inputted to two receptors of the EMD, respectively.

Then, time delay components I′(x, y, t) and I′(x+1, y, t) of timeconstant τ are calculated by the following equations:

$\begin{matrix}{{I^{\prime}\left( {x,y,t} \right)} = {{I^{\prime}\left( {x,y,{t - 1}} \right)} - {\frac{1}{\tau}\left( {{I^{\prime}\left( {x,y,{t - 1}} \right)} - {I\left( {x,y,t} \right)}} \right)}}} & (2) \\{{I^{\prime}\left( {{x + 1},y,t} \right)} = {{I^{\prime}\left( {{x + 1},y,{t - 1}} \right)} - {\frac{1}{\tau}\left( {{I^{\prime}\left( {{x + 1},y,{t - 1}} \right)} - {I\left( {{x + 1},y,t} \right)}} \right)}}} & (3)\end{matrix}$

Then, a space-time correlation value v(x, y, t) between the receptors iscalculated by the following equation:v(x,y,t)=I(x,y,t)I′(x+1,y,t)−I(x+1,y,t)I′(x,y,t)  (4)

The correlation value v(x, y, t) calculated by the equation (4) is apositive value when an object in the image moves from coordinates (x, y)to (x+1, y) (moves to the right in FIG. 4), and is a negative value whenan object in the image moves from coordinates (x+1, y) to (x, y) (movesto the left in FIG. 4). The correlation value v(x, y, t) is defined as a“motion vector” of the pixel (x, y).

A technique for calculating a motion vector v(x, y, t) may be anytechnique that can extract information about a motion direction on aper-pixel basis from an image, and may be a conventional technique suchas an optical flow technique.

Returning to FIG. 3, in step S107, the collision probability calculationsection 20 calculates “collision probabilities” at a plurality oflocations of the image based on motion vectors calculated by the motionvector calculation section 16. The “collision probability” as usedherein is an indicator of a probability that the obstacle 16 relativelyapproaches and collides with the mobile unit 10. The obstacle 16 is morelikely to collide with the mobile unit 10 as the value of “collisionprobability” increases. In the present embodiment, as a technique forcalculating a collision probability, a collision avoidance model ismodeled on the optic lobe cells of the flies having compound eyes.

As shown in FIG. 7, the collision avoidance model applies a plurality ofreceptive field units to an image, and calculates collisionprobabilities with the centers of the receptive fields being pixels(xc1, yc1), xc2, yc2), and xc3, yc3) respectively. The receptive fieldunit comprises a Mexican hat shaped filter as shown in FIG. 6C. Itproduces an excitatory response in the vicinity of the center of thefilter (an area indicated by LE in FIG. 7), and produces an inhibitoryresponse in an area apart from the center (an area indicated by LC inFIG. 7).

In the present embodiment, the receptive field unit is implemented bycombining two Gaussian filters having different variances. The receptivefield unit includes a Gaussian filter F(t) which gives a response tomotion in diverging direction and a Gaussian filter C(t) which gives aresponse to motion in converging direction.

When a pixel is at the center coordinate (x_(c), y_(c)) of a receptivefield unit, input Ve(x, y, t) to the Gaussian filter F(t) is obtainedaccording to the motion vector v(x, y, t) and the coordinate of thepixel, as expressed by the equation:

$\begin{matrix}{{v_{e}\left( {x,y,t} \right)} = \left\{ \begin{matrix}{v\left( {x,y,t} \right)} & {{{{if}\mspace{14mu} x} > x_{c}},{{v\left( {x,y,t} \right)} > 0}} \\{- {v\left( {x,y,t} \right)}} & {{{{if}\mspace{14mu} x} < x_{c}},{{v\left( {x,y,t} \right)} < 0}} \\0 & {otherwise}\end{matrix} \right.} & (5)\end{matrix}$

Output of the Gaussian filter F(t) with the center (x_(c), y_(c)) iscalculated by the following equation:

$\begin{matrix}{{F(t)} = {\sum\limits_{k}{\sum\limits_{l}{{v_{e}\left( {x_{k},y_{l},t} \right)}{\mathbb{e}}^{\sqrt{\frac{{({x_{k} - x_{c}})}^{2} + {({{y\;}_{l} - y_{c}})}^{2}}{2\;\sigma_{e}^{2}}}}}}}} & (6)\end{matrix}$where σe is a constant which defines a spatial expanse in integratingmotion vectors.

In the Gaussian filter F(t) of the equation (6), gain by which inputVex, y, t) is multiplied is determined according to the distance fromthe center (x_(c), y_(c)). For example, the gain of the Gaussian filterof the equation (6) may have a value as shown in FIG. 6A according tothe distance from the center (x_(c), y_(c)).

Then, input Vc(x, y, t) to the Gaussian filter C(t) is obtained by thefollowing equation according to the motion vector v(x, y, t) and thecoordinate of the pixel:

$\begin{matrix}{{v_{c}\left( {x,y,t} \right)} = \left\{ \begin{matrix}{- {v\left( {x,y,t} \right)}} & {{{{if}\mspace{14mu} x} > x_{c}},{{v\left( {x,y,t} \right)} < 0}} \\{v\left( {x,y,t} \right)} & {{{{if}\mspace{14mu} x} < x_{c}},{{v\left( {x,y,t} \right)} > 0}} \\0 & {otherwise}\end{matrix} \right.} & (7)\end{matrix}$

Output of the Gaussian filter C(t) with the center (x_(c), y_(c)) iscalculated by the following equation:

$\begin{matrix}{{C(t)} = {\sum\limits_{k}{\sum\limits_{l}{{v_{c}\left( {x_{k},y_{l},t} \right)}{\mathbb{e}}^{\sqrt{\frac{{({x_{k} - x_{c}})}^{2} + {({{y\;}_{l} - y_{c}})}^{2}}{2\;\sigma_{c}^{2}}}}}}}} & (8)\end{matrix}$where σc is constant which defines a spatial expanse in integratingmotion vectors.

In the Gaussian filter of the equation (8), gain by which input Vc(x, y,t) is multiplied is determined according to the distance from the center(x_(c), y_(c)). For example, the gain of the Gaussian filter of theequation (8) may have a value as shown in FIG. 6B according to thedistance from the center (x_(c), y_(c)).

Finally, a difference between these two filters is calculated to obtaina receptive field unit RF 0(t) as follows:0(t)=F(t)−a·C(t)  (9)where a is a constant and satisfies 0<a<1.

The receptive field unit RF 0(t) thus obtained in the present embodimentbecomes a Mexican hat shaped filter as shown in FIG. 6C. The receptivefield unit RF 0(t) produces an excitatory response in the central area(LE) with the center (x_(c), y_(c)) to a motion vector diverging fromthe center (x_(c), y_(c)), and produces an inhibitory response in thearea surrounding LE (LC) to a motion vector converging toward the center(x_(c), y_(c)).

While receptive field units assumes a Mexican hat shape as shown in FIG.3C, they may be designed using a technique other than the Gaussianfilter.

In the collision avoidance model according to the present embodiment,three receptive field units produced according to the equations (5) to(9) are positioned in parallel to the x-axis direction as shown in FIG.7. The outputs 01(t), 02(t), and 03(t) of the receptive field units RF01, RF 02 and RF 03 with the centers at (xc1, yc1), (xc2, yc2), and(xc3, yc3) respectively are calculated. The calculated values aredefined as “collision probabilities” at the receptive fields with thecenters at (xc1, yc1), (xc2, yc2), and (xc3, yc3) respectively.

Returning to FIG. 3, in step S109, the status determination section 22determines whether or not any of the outputs 01(t), 02(t), and 03(t) isgreater than or equal to a threshold value (for example, 0.02). When anyof the outputs 01(t), 02(t), and 03(t) is greater than or equal to thethreshold value, it is determined that the obstacle 16 is highly likelyto collide with the mobile unit 10, and the processing proceeds to stepS111. When all of the outputs 01(t), 02(t), and 03(t) are below thethreshold value, it is determined that the obstacle 16 is less likely tocollide yet so that avoidance movement is not needed, and the processingis terminated.

In step S111, the status determination section 22 compares the outputs01(t), 02(t), and 03(t) to determine a direction in which the mobileunit 10 needs to move. The status determination section 22 selects adirection θ in which the mobile unit needs to move to avoid collisionwith the obstacle, for example, by the following condition. As shown inFIG. 1, θ equals 0 in the front direction, and a clockwise directionfrom there is a positive direction.

$\begin{matrix}{\theta = \left\{ \begin{matrix}{- 45} & {{{if}\mspace{14mu} 01} \leq {02\mspace{14mu}{and}\mspace{14mu} 01} < 03} \\0 & {{{if}\mspace{14mu} 02} \leq {01\mspace{14mu}{and}\mspace{14mu} 02} < 03} \\45 & {{{if}\mspace{14mu} 03} \leq {01\mspace{14mu}{and}\mspace{14mu} 03} < 02} \\180 & {otherwise}\end{matrix} \right.} & (10)\end{matrix}$

The condition expression (10) is set such that the receptive field unitwhose output value is minimum among the outputs 01(t), 02(t), and 03(t)is selected to give the moving direction θ of the mobile unit 10.Therefore, the mobile unit 10 is moved in a direction in which themobile unit 10 is least likely to collide with the obstacle 16.

Then, in the step S113, the mobile unit 10 is moved in the angulardirection determined in the step S111 to avoid collision with theobstacle 16.

Experimental results of the collision avoidance system according to thepresent embodiment will be described next with reference to FIG. 8.

FIG. 8 is a graph which shows progressions of the outputs 01(t), 02(t),and 03(t) in the time as the obstacle 16 approaches the mobile unit 10from the front (θ=0). The abscissa of the graph represents time tocollision, and time 0, time −1, and time −2 indicates the time ofcollision, one second before the collision, and two seconds beforecollision respectively. This “time to collision” is derived forverification in advance according to a relative movement speed of theobstacles 16 and a distance from the obstacles 16 to the mobile unit.The ordinate of the graph represents output of a receptive field unit.

With reference to FIG. 8, output 02 of the receptive field unit RF 02 inthe center exceeds a threshold value more than one second before thecollision. With reference to step S109 of FIG. 3, the statusdetermination section 22 determines that the obstacle 16 is highlylikely to collide with the mobile unit 10 at this time point.

A moving direction θ of the mobile unit 10 is determined based on thecondition expression (10) described in step S111 of FIG. 3. Because theoutputs 0(t), 02(t), and 03(t) are in a condition that 03(t)<01(t) and03(t)<02(t), the status determination section 22 selects the directionof the receptive field unit RF 03 with the output 03(t). The mobile unit10 is least likely to collide with the obstacle 16 in the direction of45 degrees from the front to the right. The mobile unit 10 moves in theselected direction to avoid the collision with the obstacle 16.

As described above, since probabilities that the obstacle 16 collideswith the mobile unit 10 are represented by the outputs 01(t), 02(t), and03(t) based on a plurality of pixels in an image, and the direction inwhich an obstacle is approaching the mobile unit is determined based onthese plural collision probabilities 01(t), 02(t), and 03(t), the mobileunit 10 can avoid collision with the obstacle 16 by selecting theoptimal moving direction θ.

Embodiments of the present invention are not limited to the abovedescribed embodiment and can be modified without departing from thespirit of the present invention.

Although the case where the obstacle 16 approaches the mobile unit 10has been described in the above described embodiments, the collisionavoidance technique according to the present invention can also beapplied to the case where the mobile unit 10 moves toward a fixedobstacle, and the case where the mobile unit 10 as well as the obstacle16 move.

Although the small mobile robot is described as a specific example ofthe mobile unit 10 in the above embodiment, the mobile unit 10 of thepresent invention is not limited to the small mobile robot, and may be,for example, a bipedal walking robot or an automobile.

1. A collision avoidance system for a mobile unit for avoiding collisionwith an obstacle, the system comprising: means for capturing an image ofan environment of the mobile unit; means for calculating motion vectorsrelative to the image captured by said image capturing means; means forcalculating collision probabilities with the obstacle based on themotion vectors on a plurality of pixels in said image; and means forcomparing the collision probabilities on the plurality of pixels todetermine a direction in which said obstacle relatively approaches themobile unit, wherein said means for calculating collision probabilitycomprises a plurality of receptive field units that are modeled on theoptic lobe cells of flies.
 2. The system according to claim 1, whereinsaid means for calculating motion vectors calculates a temporalcorrelation of intensity of two different pixels in said image todetermine the motion vector.
 3. The system according to claim 1, whereineach of the receptive field units comprises a filter producing anexcitatory response to the motion vector diverging from the center ofthe receptive field in the central area of the receptive field andproducing an inhibitory response to the motion vector converging towardthe center of the receptive field in the areas around the central area.4. The system according to claim 3, further comprises means fordetermining a direction in which said obstacle approaches said mobileunit based on the outputs of the receptive field units when one of theoutputs exceeds a predetermined threshold value.
 5. The system accordingto claim 3, further comprises means for determining the direction thatmobile unit needs to move to avoid collision with the obstacle based onthe outputs of the receptive field units.
 6. A non-transitory computerreadable recording medium storing a computer program, which, when run ona computer installed in a mobile unit, performs: capturing an image ofan environment of the mobile unit; calculating motion vectors relativeto the captured image; calculating collision probabilities with theobstacle based on the motion vectors on a plurality of pixels in saidimage; and comparing the collision probabilities on the plurality ofpixels to determine a direction in which said obstacle relativelyapproaches the mobile unit, wherein said calculating collisionprobability includes a plurality of receptive field units that aremodeled on the optic lobe cells of flies.
 7. The medium according toclaim 6, wherein said calculating motion vectors includes calculating atemporal correlation of intensity of two different pixels in said imageto determine the motion vector.
 8. The medium according to claim 6,wherein each of the receptive field units comprises a filter producingan excitatory response to the motion vector diverging from the center ofthe receptive field in the central area of the receptive field andproducing an inhibitory response to the motion vector converging towardthe center of the receptive field in the areas around the central area.9. The medium according to claim 8, wherein the program further performsdetermining a direction in which said obstacle approaches said mobileunit based on the outputs of the receptive field units when one of theoutputs exceeds a predetermined threshold value.
 10. The mediumaccording to claim 8, wherein the program further performs determiningthe direction that mobile unit needs to move to avoid collision with theobstacle based on the outputs of the receptive field units.
 11. A methodfor avoiding collision with an obstacle approaching a mobile unit,comprising the steps of: capturing an image of an environment of themobile unit; calculating motion vectors relative to the captured image;calculating collision probabilities with the obstacle based on themotion vectors on a plurality of pixels in said image; and comparing thecollision probabilities on the plurality of pixels to determine adirection in which said obstacle relatively approaches the mobile unit,wherein said calculating collision probability includes a plurality ofreceptive field units that are modeled on the optic lobe cells of flies.12. The method according to claim 11, wherein said calculating motionvectors includes calculating a temporal correlation of intensity of twodifferent pixels in said image to determine the motion vector.
 13. Themethod according to claim 11, wherein each of the receptive field unitscomprises a filter producing an excitatory response to the motion vectordiverging from the center of the receptive field in the central area ofthe receptive field and producing an inhibitory response to the motionvector converging toward the center of the receptive field in the areasaround the central area.
 14. The method according to claim 13, whereinthe program further performs determining a direction in which saidobstacle approaches said mobile unit based on the outputs of thereceptive field units when one of the outputs exceeds a predeterminedthreshold value.
 15. The method according to claim 13, wherein theprogram further performs determining the direction that mobile unitneeds to move to avoid collision with the obstacle based on the outputsof the receptive field units.